A Computationally Efficient Ensemble Boosting Framework for Optimized DoS Attack Detection in Wireless Sensor Networks

Abstract

As Wireless Sensor Networks (WSNs) become more prevalent in modern IoT deployments, Denial of Service (DoS) attacks, which are capable of exhausting node resources and disrupting entire network segments, have emerged as their most significant security threat. In this research, we propose a computationally efficient intrusion detection framework based on the eXtreme Gradient Boosting (XGBoost) model, specifically tailored for energy-constrained environments, and designed for deployment at the cluster-head or gateway levels of WSN architectures. The framework integrates a systematic feature selection process that reduces the initial 19-column curated dataset to an optimal 16-feature subset, addressing correlation and redundancy while maintaining high-fidelity detection. Unlike many existing studies that optimize for accuracy alone, this work uniquely combines four complementary evaluation dimensions: classification accuracy, per-fold stability (standard deviation), ultra-low prediction latency, and adaptive threshold analysis for deployment flexibility. We validated our results through five-fold cross-validation on the WSN-DS dataset. The proposed framework achieved a mean accuracy of 99.74% with an exceptionally low standard deviation of ±0.000174, a prediction latency of 0.0017 ms per sample, and demonstrated a 20× computational efficiency advantage over state-of-the-art ensemble learners like CatBoost, with a compressed model size of 235 KB and an estimated inference cost of 2,550 FLOPs per prediction. The overall outcomes of the study support that the proposed framework provides a resilient, adaptable solution for real-world WSN deployment